Preference Understanding

Preference understanding aims to model and predict user choices, preferences, and intents from various data sources, including text, interactions, and explicit feedback. Current research focuses on leveraging large language models and reinforcement learning techniques, often incorporating preference-based methods and disentangled representations to improve accuracy and interpretability. This field is crucial for advancing personalized recommendation systems, human-computer interaction, and the development of more aligned and helpful artificial intelligence systems.